Based on a previous work on handwritten Japanese kanji character recognition, a postprocessing system for handwritten Japanese address recognition is proposed. Basically, the recognition system is composed of CombNET-II, a general-purpose large-scale character recognizer and MMVA, a modified majority voting system. Beginning with a set of character candidates, produced by a character recognizer for each character that composes the input word and a lexicon, an interpretation to the input word is generated. MMVA is used in the postprocessing stage to select the interpretation that accumulates the highest score. In the case of more than one possible interpretation, the Conflict Analyzing System calls the character recognizer again to generate scores for each character that composes each interpretation to determine the final output word. The proposed word recognition system was tested with 2 sets of handwritten Japanese city names, and recognition rates higher than 99% were achieved, demonstrating the effectiveness of the method.

A computational cost reduction scheme for a post-distortion type nonlinear distortion compensator of OFDM signals is proposed, and compared with the conventional sub-optimum detection scheme. The proposed scheme utilizes the principle that a complex OFDM signal can be demodulated with not only both I-phase (real part) and Q-phase (imaginary part) components, but also either of them. Usually each phase of an OFDM signal exhibits different signal envelope and they are distorted differently by the nonlinearity of a power amplifier. Consequently, three output sequence patterns can be obtained at the receiver. By comparing these outputs, we can know the erroneous positions of these sequences to some extent. By the aid of this comparison, we need to evaluate only a limited number of replicas for the compensation process of the post-distortion type nonlinear distortion compensator, which results in the computational cost reduction. We have proposed four new compensation schemes based on this idea and derived their performance in terms of the bit error rate and the average number of calculations.

This paper addresses the important issue of estimating realistic grasping postures, and presents a methodology and algorithm to automate the generation of hand and body postures during the grasp of arbitrary shaped objects. Predefined body postures stored in a database are generalized to adapt to a specific grasp using inverse kinematics. The reachable space is represented discretely dividing into small subvolumes, which enables to construct the database. The paper also addresses some common problems of articulated figure animation. A new approach for body positioning with kinematic constraints on both hands is described. An efficient and accurate manipulation of joint constraints is presented. Obtained results are quite satisfactory, and some of them are shown in the paper. The proposed algorithms can find application in the motion of virtual actors, all kinds of animation systems including human motion, robotics and some other fields such as medicine, for instance, to move the artificial limbs of handicapped people in a natural way.

A new postprocessing method using interpolated n-gram model for Japanese documents is proposed. The method has the advantages over conventional approaches in enabling high-speed, knowledge-free processing. In parameter estimation of an n-gram model for a large size of vocabulary, it is difficult to obtain sufficient training samples. To overcome poverty of samples, two smoothing methods for Japanese character trigram model are evaluated, and the superiority of deleted interpolation method is shown by using perplexity. A document recognition system based on the trigram model is constructed, which finds maximum likelihood solutions through Viterbi algorithm. Experimental results for three kinds of documents show that the performance is high when using deleted interpolation method for smoothing. 90% of OCR errors are corrected for the documents similar to training text data, and 75% of errors are corrected for the documents not so similar to training text data.

Optical Feedering between Base Stations and Control Station is an effective technique for future microcellular mobile communication systems. The use of Laser Diode (LD) in such a system leads to the generation of intermodulation products, which consequently affect a system performance and ultimately restrain the maximum number of users that the system can serve. The problem becomes further intensified in case of CDMA system which is a candidate for future cellular mobile and personal communication systems. In this paper LD's Nonlinearity compensation technique for Direct Sequence spread spectrum CDMA signals in optical communication system is presented. This technique involves the implementation of a nonlinear block herein after called Post Nonlinearity Recovery Block (PNRB). This block is designed to exhibit the characteristics inverse to those of LD. The block is designed theoretically by deriving the complete expressions for Transfer functions. Some useful results of theoretical investigation of a proposed scheme have been presented, which form the basis for the experimental test system. The work is novel because, (i) Compensation analysis has been carried out for DS-CDMA signals for the first time, and (ii) Compensation has been proposed on the control station instead of base station, which is different from the conventional techniques and offers several additional advantages. Performance of the system with and without PNRB is evaluated by Intermodulation Distortion (IMD) and SNR Analysis. The results show that LDs' nonlinearity distortion level can be compensated to a remarkable extent.

This paper presents a simple and efficient method for estimation of parameters useful for textured image analysis. On the basia of a 2-D Wold-like decomposition of homogenenous random fields, the texture field can be decomposed into a sum of two mutually orthogonal components: a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. The parameters representing the ellipse and those representing the delta functions can be used to detect rotation angles and scaling factors of test textures. Specially, rotation and scaling invariant parameters, which are applicable to the classification of rotated and scaled textured images, can be estimated by combining these parameters. That is, a test texture can be correctly classified even if it is rotated and scaled. Several computer experiments on natural textures show the effectiveness of this method.

In this paper, we propose the media scheduler employing an adaptive estimator, which uses a posteriori information of data traffic characteristics to facilitate scheduling, when available, to provide on-line scheduling of dynamic scene change based on its statistical characteristics. Especially, a new adaptive scheduling scheme showed good persistent to the arrival message with bursty characteristics. And we confirmed the performance through the computer simulation when QOS requirements are given.

An integrated pen interface system was developed to allow effective Japanese text entry. It consists of sub-systems for handwriting recognition, contextual post-processing, and enhanced Kana-to-Kanji conversion. The recognition sub-system uses a hybrid algorithm consisting of a pattern matcher and a neural network discriminator. Special care was taken to improve the recognition of non-Kanji and simple Kanji characters frequently used in fast data entry. The post-processor predicts consecutive characters on the basis of bigrams modified by the addition of parts of speech and substitution of macro characters for Kanji characters. A Kana-to Kanji conversion method designed for ease of use with a pen interface has also been integrated into the system. In an experiment in which 2,900 samples of Kanji and non-Kanji characters were obtained from 20 subjects, it was observed that the original recognition accuracy of 83.7% (the result obtained by using the pattern matching recognizer) was improved to 90.7% by adding the neural network discriminator, and that it was further improved to 94.4% by adding the post-processor. The improved recognition accuracy for non-Kanji characters was particularly marked.

We have aimed at constructing a forward dynamics model (FDM) of the human arm in the form of an artificial neural network while recordings of EMG and movement trajectories. We succeeded in: (1) estimating the joint torques under isometric conditions and (2) estimating trajectories from surface EMG signals in the horizontal plane. The human arm has seven degrees of freedom: the shoulder has three, the elbow has one and the wrist has three. Only two degrees of freedom were considered in the previous work. Moreover, the arm was supported horizontally. So, free movement in 3D space is still a necessity. And for 3D movements or posture control, compensation for gravity has to be considered. In this papre, four joint angles, one at the elbow and three at the shoulder were estimated from surface EMG signals of 12 flexor and extensor muscles during posture control in 3D space.